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            <td width="10%" class="headerItem">Current view:</td>
            <td width="35%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">src/caffe/layers</a> - scale_layer.cpp<span style="font-size: 80%;"> (source / <a href="scale_layer.cpp.func-sort-c.html">functions</a>)</span></td>
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            <td width="10%" class="headerCovTableHead">Hit</td>
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            <td width="15%" class="headerCovTableHead">Coverage</td>
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            <td class="headerItem">Test:</td>
            <td class="headerValue">code analysis</td>
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            <td class="headerItem">Lines:</td>
            <td class="headerCovTableEntry">2</td>
            <td class="headerCovTableEntry">126</td>
            <td class="headerCovTableEntryLo">1.6 %</td>
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            <td class="headerItem">Date:</td>
            <td class="headerValue">2020-09-11 22:50:33</td>
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            <td class="headerItem">Functions:</td>
            <td class="headerCovTableEntry">2</td>
            <td class="headerCovTableEntry">16</td>
            <td class="headerCovTableEntryLo">12.5 %</td>
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            <td class="headerValueLeg">            Lines:
            <span class="coverLegendCov">hit</span>
            <span class="coverLegendNoCov">not hit</span>
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<pre class="sourceHeading">          Line data    Source code</pre>
<pre class="source">
<a name="1"><span class="lineNum">       1 </span>            : #include &lt;algorithm&gt;</a>
<span class="lineNum">       2 </span>            : #include &lt;vector&gt;
<span class="lineNum">       3 </span>            : 
<span class="lineNum">       4 </span>            : #include &quot;caffe/filler.hpp&quot;
<span class="lineNum">       5 </span>            : #include &quot;caffe/layer_factory.hpp&quot;
<span class="lineNum">       6 </span>            : #include &quot;caffe/layers/scale_layer.hpp&quot;
<span class="lineNum">       7 </span>            : #include &quot;caffe/util/math_functions.hpp&quot;
<span class="lineNum">       8 </span>            : 
<span class="lineNum">       9 </span>            : namespace caffe {
<span class="lineNum">      10 </span>            : 
<span class="lineNum">      11 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">      12 </span><span class="lineNoCov">          0 : void ScaleLayer&lt;Dtype&gt;::LayerSetUp(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom,</span>
<span class="lineNum">      13 </span>            :       const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top) {
<span class="lineNum">      14 </span>            :   const ScaleParameter&amp; param = this-&gt;layer_param_.scale_param();
<span class="lineNum">      15 </span><span class="lineNoCov">          0 :   if (bottom.size() == 1 &amp;&amp; this-&gt;blobs_.size() &gt; 0) {</span>
<span class="lineNum">      16 </span><span class="lineNoCov">          0 :     LOG(INFO) &lt;&lt; &quot;Skipping parameter initialization&quot;;</span>
<span class="lineNum">      17 </span><span class="lineNoCov">          0 :   } else if (bottom.size() == 1) {</span>
<span class="lineNum">      18 </span>            :     // scale is a learned parameter; initialize it
<span class="lineNum">      19 </span><span class="lineNoCov">          0 :     axis_ = bottom[0]-&gt;CanonicalAxisIndex(param.axis());</span>
<span class="lineNum">      20 </span>            :     const int num_axes = param.num_axes();
<span class="lineNum">      21 </span><span class="lineNoCov">          0 :     CHECK_GE(num_axes, -1) &lt;&lt; &quot;num_axes must be non-negative, &quot;</span>
<span class="lineNum">      22 </span>            :                            &lt;&lt; &quot;or -1 to extend to the end of bottom[0]&quot;;
<span class="lineNum">      23 </span><span class="lineNoCov">          0 :     if (num_axes &gt;= 0) {</span>
<span class="lineNum">      24 </span><span class="lineNoCov">          0 :       CHECK_GE(bottom[0]-&gt;num_axes(), axis_ + num_axes)</span>
<span class="lineNum">      25 </span>            :           &lt;&lt; &quot;scale blob's shape extends past bottom[0]'s shape when applied &quot;
<span class="lineNum">      26 </span><span class="lineNoCov">          0 :           &lt;&lt; &quot;starting with bottom[0] axis = &quot; &lt;&lt; axis_;</span>
<span class="lineNum">      27 </span>            :     }
<span class="lineNum">      28 </span><span class="lineNoCov">          0 :     this-&gt;blobs_.resize(1);</span>
<span class="lineNum">      29 </span>            :     const vector&lt;int&gt;::const_iterator&amp; shape_start =
<span class="lineNum">      30 </span><span class="lineNoCov">          0 :         bottom[0]-&gt;shape().begin() + axis_;</span>
<span class="lineNum">      31 </span>            :     const vector&lt;int&gt;::const_iterator&amp; shape_end =
<span class="lineNum">      32 </span><span class="lineNoCov">          0 :         (num_axes == -1) ? bottom[0]-&gt;shape().end() : (shape_start + num_axes);</span>
<span class="lineNum">      33 </span><span class="lineNoCov">          0 :     vector&lt;int&gt; scale_shape(shape_start, shape_end);</span>
<span class="lineNum">      34 </span><span class="lineNoCov">          0 :     this-&gt;blobs_[0].reset(new Blob&lt;Dtype&gt;(scale_shape));</span>
<span class="lineNum">      35 </span><span class="lineNoCov">          0 :     FillerParameter filler_param(param.filler());</span>
<span class="lineNum">      36 </span><span class="lineNoCov">          0 :     if (!param.has_filler()) {</span>
<span class="lineNum">      37 </span>            :       // Default to unit (1) filler for identity operation.
<span class="lineNum">      38 </span><span class="lineNoCov">          0 :       filler_param.set_type(&quot;constant&quot;);</span>
<span class="lineNum">      39 </span>            :       filler_param.set_value(1);
<span class="lineNum">      40 </span>            :     }
<span class="lineNum">      41 </span><span class="lineNoCov">          0 :     shared_ptr&lt;Filler&lt;Dtype&gt; &gt; filler(GetFiller&lt;Dtype&gt;(filler_param));</span>
<span class="lineNum">      42 </span><span class="lineNoCov">          0 :     filler-&gt;Fill(this-&gt;blobs_[0].get());</span>
<span class="lineNum">      43 </span>            :   }
<span class="lineNum">      44 </span><span class="lineNoCov">          0 :   if (param.bias_term()) {</span>
<span class="lineNum">      45 </span><span class="lineNoCov">          0 :     LayerParameter layer_param(this-&gt;layer_param_);</span>
<span class="lineNum">      46 </span><span class="lineNoCov">          0 :     layer_param.set_type(&quot;Bias&quot;);</span>
<span class="lineNum">      47 </span><span class="lineNoCov">          0 :     BiasParameter* bias_param = layer_param.mutable_bias_param();</span>
<span class="lineNum">      48 </span>            :     bias_param-&gt;set_axis(param.axis());
<span class="lineNum">      49 </span><span class="lineNoCov">          0 :     if (bottom.size() &gt; 1) {</span>
<span class="lineNum">      50 </span><span class="lineNoCov">          0 :       bias_param-&gt;set_num_axes(bottom[1]-&gt;num_axes());</span>
<span class="lineNum">      51 </span>            :     } else {
<span class="lineNum">      52 </span>            :       bias_param-&gt;set_num_axes(param.num_axes());
<span class="lineNum">      53 </span>            :     }
<span class="lineNum">      54 </span><span class="lineNoCov">          0 :     bias_param-&gt;mutable_filler()-&gt;CopyFrom(param.bias_filler());</span>
<span class="lineNum">      55 </span><span class="lineNoCov">          0 :     bias_layer_ = LayerRegistry&lt;Dtype&gt;::CreateLayer(layer_param);</span>
<span class="lineNum">      56 </span><span class="lineNoCov">          0 :     bias_bottom_vec_.resize(1);</span>
<span class="lineNum">      57 </span><span class="lineNoCov">          0 :     bias_bottom_vec_[0] = bottom[0];</span>
<span class="lineNum">      58 </span><span class="lineNoCov">          0 :     bias_layer_-&gt;SetUp(bias_bottom_vec_, top);</span>
<span class="lineNum">      59 </span><span class="lineNoCov">          0 :     if (this-&gt;blobs_.size() + bottom.size() &lt; 3) {</span>
<span class="lineNum">      60 </span>            :       // case: blobs.size == 1 &amp;&amp; bottom.size == 1
<span class="lineNum">      61 </span>            :       // or blobs.size == 0 &amp;&amp; bottom.size == 2
<span class="lineNum">      62 </span><span class="lineNoCov">          0 :       bias_param_id_ = this-&gt;blobs_.size();</span>
<span class="lineNum">      63 </span><span class="lineNoCov">          0 :       this-&gt;blobs_.resize(bias_param_id_ + 1);</span>
<span class="lineNum">      64 </span><span class="lineNoCov">          0 :       this-&gt;blobs_[bias_param_id_] = bias_layer_-&gt;blobs()[0];</span>
<span class="lineNum">      65 </span>            :     } else {
<span class="lineNum">      66 </span>            :       // bias param already initialized
<span class="lineNum">      67 </span><span class="lineNoCov">          0 :       bias_param_id_ = this-&gt;blobs_.size() - 1;</span>
<span class="lineNum">      68 </span><span class="lineNoCov">          0 :       bias_layer_-&gt;blobs()[0] = this-&gt;blobs_[bias_param_id_];</span>
<span class="lineNum">      69 </span>            :     }
<span class="lineNum">      70 </span><span class="lineNoCov">          0 :     bias_propagate_down_.resize(1, false);</span>
<span class="lineNum">      71 </span>            :   }
<span class="lineNum">      72 </span><span class="lineNoCov">          0 :   this-&gt;param_propagate_down_.resize(this-&gt;blobs_.size(), true);</span>
<span class="lineNum">      73 </span><span class="lineNoCov">          0 : }</span>
<span class="lineNum">      74 </span>            : 
<span class="lineNum">      75 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">      76 </span><span class="lineNoCov">          0 : void ScaleLayer&lt;Dtype&gt;::Reshape(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom,</span>
<span class="lineNum">      77 </span>            :       const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top) {
<span class="lineNum">      78 </span>            :   const ScaleParameter&amp; param = this-&gt;layer_param_.scale_param();
<span class="lineNum">      79 </span><span class="lineNoCov">          0 :   Blob&lt;Dtype&gt;* scale = (bottom.size() &gt; 1) ? bottom[1] : this-&gt;blobs_[0].get();</span>
<span class="lineNum">      80 </span>            :   // Always set axis_ == 0 in special case where scale is a scalar
<span class="lineNum">      81 </span>            :   // (num_axes == 0). Mathematically equivalent for any choice of axis_, so the
<span class="lineNum">      82 </span>            :   // actual setting can be safely ignored; and computation is most efficient
<span class="lineNum">      83 </span>            :   // with axis_ == 0 and (therefore) outer_dim_ == 1. (Setting axis_ to
<span class="lineNum">      84 </span>            :   // bottom[0]-&gt;num_axes() - 1, giving inner_dim_ == 1, would be equally
<span class="lineNum">      85 </span>            :   // performant.)
<span class="lineNum">      86 </span><span class="lineNoCov">          0 :   axis_ = (scale-&gt;num_axes() == 0) ?</span>
<span class="lineNum">      87 </span>            :       0 : bottom[0]-&gt;CanonicalAxisIndex(param.axis());
<span class="lineNum">      88 </span><span class="lineNoCov">          0 :   CHECK_GE(bottom[0]-&gt;num_axes(), axis_ + scale-&gt;num_axes())</span>
<span class="lineNum">      89 </span>            :       &lt;&lt; &quot;scale blob's shape extends past bottom[0]'s shape when applied &quot;
<span class="lineNum">      90 </span><span class="lineNoCov">          0 :       &lt;&lt; &quot;starting with bottom[0] axis = &quot; &lt;&lt; axis_;</span>
<span class="lineNum">      91 </span><span class="lineNoCov">          0 :   for (int i = 0; i &lt; scale-&gt;num_axes(); ++i) {</span>
<span class="lineNum">      92 </span><span class="lineNoCov">          0 :     CHECK_EQ(bottom[0]-&gt;shape(axis_ + i), scale-&gt;shape(i))</span>
<span class="lineNum">      93 </span><span class="lineNoCov">          0 :         &lt;&lt; &quot;dimension mismatch between bottom[0]-&gt;shape(&quot; &lt;&lt; axis_ + i</span>
<span class="lineNum">      94 </span><span class="lineNoCov">          0 :         &lt;&lt; &quot;) and scale-&gt;shape(&quot; &lt;&lt; i &lt;&lt; &quot;)&quot;;</span>
<span class="lineNum">      95 </span>            :   }
<span class="lineNum">      96 </span><span class="lineNoCov">          0 :   outer_dim_ = bottom[0]-&gt;count(0, axis_);</span>
<span class="lineNum">      97 </span><span class="lineNoCov">          0 :   scale_dim_ = scale-&gt;count();</span>
<span class="lineNum">      98 </span><span class="lineNoCov">          0 :   inner_dim_ = bottom[0]-&gt;count(axis_ + scale-&gt;num_axes());</span>
<span class="lineNum">      99 </span><span class="lineNoCov">          0 :   if (bottom[0] == top[0]) {  // in-place computation</span>
<span class="lineNum">     100 </span><span class="lineNoCov">          0 :     temp_.ReshapeLike(*bottom[0]);</span>
<span class="lineNum">     101 </span>            :   } else {
<span class="lineNum">     102 </span><span class="lineNoCov">          0 :     top[0]-&gt;ReshapeLike(*bottom[0]);</span>
<span class="lineNum">     103 </span>            :   }
<span class="lineNum">     104 </span><span class="lineNoCov">          0 :   sum_result_.Reshape(vector&lt;int&gt;(1, outer_dim_ * scale_dim_));</span>
<span class="lineNum">     105 </span><span class="lineNoCov">          0 :   const int sum_mult_size = std::max(outer_dim_, inner_dim_);</span>
<span class="lineNum">     106 </span><span class="lineNoCov">          0 :   sum_multiplier_.Reshape(vector&lt;int&gt;(1, sum_mult_size));</span>
<span class="lineNum">     107 </span><span class="lineNoCov">          0 :   if (sum_multiplier_.cpu_data()[sum_mult_size - 1] != Dtype(1)) {</span>
<span class="lineNum">     108 </span><span class="lineNoCov">          0 :     caffe_set(sum_mult_size, Dtype(1), sum_multiplier_.mutable_cpu_data());</span>
<span class="lineNum">     109 </span>            :   }
<span class="lineNum">     110 </span><span class="lineNoCov">          0 :   if (bias_layer_) {</span>
<span class="lineNum">     111 </span><span class="lineNoCov">          0 :     bias_bottom_vec_[0] = top[0];</span>
<span class="lineNum">     112 </span><span class="lineNoCov">          0 :     bias_layer_-&gt;Reshape(bias_bottom_vec_, top);</span>
<span class="lineNum">     113 </span>            :   }
<span class="lineNum">     114 </span><span class="lineNoCov">          0 : }</span>
<span class="lineNum">     115 </span>            : 
<span class="lineNum">     116 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">     117 </span><span class="lineNoCov">          0 : void ScaleLayer&lt;Dtype&gt;::Forward_cpu(</span>
<span class="lineNum">     118 </span>            :     const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom, const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top) {
<span class="lineNum">     119 </span><span class="lineNoCov">          0 :   const Dtype* bottom_data = bottom[0]-&gt;cpu_data();</span>
<span class="lineNum">     120 </span><span class="lineNoCov">          0 :   if (bottom[0] == top[0]) {</span>
<span class="lineNum">     121 </span>            :     // In-place computation; need to store bottom data before overwriting it.
<span class="lineNum">     122 </span>            :     // Note that this is only necessary for Backward; we could skip this if not
<span class="lineNum">     123 </span>            :     // doing Backward, but Caffe currently provides no way of knowing whether
<span class="lineNum">     124 </span>            :     // we'll need to do Backward at the time of the Forward call.
<span class="lineNum">     125 </span><span class="lineNoCov">          0 :     caffe_copy(bottom[0]-&gt;count(), bottom[0]-&gt;cpu_data(),</span>
<span class="lineNum">     126 </span>            :                temp_.mutable_cpu_data());
<span class="lineNum">     127 </span>            :   }
<span class="lineNum">     128 </span>            :   const Dtype* scale_data =
<span class="lineNum">     129 </span><span class="lineNoCov">          0 :       ((bottom.size() &gt; 1) ? bottom[1] : this-&gt;blobs_[0].get())-&gt;cpu_data();</span>
<span class="lineNum">     130 </span><span class="lineNoCov">          0 :   Dtype* top_data = top[0]-&gt;mutable_cpu_data();</span>
<span class="lineNum">     131 </span><span class="lineNoCov">          0 :   for (int n = 0; n &lt; outer_dim_; ++n) {</span>
<span class="lineNum">     132 </span><span class="lineNoCov">          0 :     for (int d = 0; d &lt; scale_dim_; ++d) {</span>
<span class="lineNum">     133 </span><span class="lineNoCov">          0 :       const Dtype factor = scale_data[d];</span>
<span class="lineNum">     134 </span><span class="lineNoCov">          0 :       caffe_cpu_scale(inner_dim_, factor, bottom_data, top_data);</span>
<span class="lineNum">     135 </span><span class="lineNoCov">          0 :       bottom_data += inner_dim_;</span>
<span class="lineNum">     136 </span><span class="lineNoCov">          0 :       top_data += inner_dim_;</span>
<span class="lineNum">     137 </span>            :     }
<span class="lineNum">     138 </span>            :   }
<span class="lineNum">     139 </span><span class="lineNoCov">          0 :   if (bias_layer_) {</span>
<span class="lineNum">     140 </span><span class="lineNoCov">          0 :     bias_layer_-&gt;Forward(bias_bottom_vec_, top);</span>
<span class="lineNum">     141 </span>            :   }
<span class="lineNum">     142 </span><span class="lineNoCov">          0 : }</span>
<span class="lineNum">     143 </span>            : 
<span class="lineNum">     144 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">     145 </span><span class="lineNoCov">          0 : void ScaleLayer&lt;Dtype&gt;::Backward_cpu(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top,</span>
<span class="lineNum">     146 </span>            :     const vector&lt;bool&gt;&amp; propagate_down, const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom) {
<span class="lineNum">     147 </span><span class="lineNoCov">          0 :   if (bias_layer_ &amp;&amp;</span>
<span class="lineNum">     148 </span><span class="lineNoCov">          0 :       this-&gt;param_propagate_down_[this-&gt;param_propagate_down_.size() - 1]) {</span>
<span class="lineNum">     149 </span><span class="lineNoCov">          0 :     bias_layer_-&gt;Backward(top, bias_propagate_down_, bias_bottom_vec_);</span>
<span class="lineNum">     150 </span>            :   }
<span class="lineNum">     151 </span><span class="lineNoCov">          0 :   const bool scale_param = (bottom.size() == 1);</span>
<span class="lineNum">     152 </span><span class="lineNoCov">          0 :   Blob&lt;Dtype&gt;* scale = scale_param ? this-&gt;blobs_[0].get() : bottom[1];</span>
<span class="lineNum">     153 </span><span class="lineNoCov">          0 :   if ((!scale_param &amp;&amp; propagate_down[1]) ||</span>
<span class="lineNum">     154 </span>            :       (scale_param &amp;&amp; this-&gt;param_propagate_down_[0])) {
<span class="lineNum">     155 </span><span class="lineNoCov">          0 :     const Dtype* top_diff = top[0]-&gt;cpu_diff();</span>
<span class="lineNum">     156 </span><span class="lineNoCov">          0 :     const bool in_place = (bottom[0] == top[0]);</span>
<span class="lineNum">     157 </span><span class="lineNoCov">          0 :     const Dtype* bottom_data = (in_place ? &amp;temp_ : bottom[0])-&gt;cpu_data();</span>
<span class="lineNum">     158 </span>            :     // Hack: store big eltwise product in bottom[0] diff, except in the special
<span class="lineNum">     159 </span>            :     // case where this layer itself does the eltwise product, in which case we
<span class="lineNum">     160 </span>            :     // can store it directly in the scale diff, and we're done.
<span class="lineNum">     161 </span>            :     // If we're computing in-place (and not doing eltwise computation), this
<span class="lineNum">     162 </span>            :     // hack doesn't work and we store the product in temp_.
<span class="lineNum">     163 </span><span class="lineNoCov">          0 :     const bool is_eltwise = (bottom[0]-&gt;count() == scale-&gt;count());</span>
<span class="lineNum">     164 </span>            :     Dtype* product = (is_eltwise ? scale-&gt;mutable_cpu_diff() :
<span class="lineNum">     165 </span><span class="lineNoCov">          0 :         (in_place ? temp_.mutable_cpu_data() : bottom[0]-&gt;mutable_cpu_diff()));</span>
<span class="lineNum">     166 </span><span class="lineNoCov">          0 :     caffe_mul(top[0]-&gt;count(), top_diff, bottom_data, product);</span>
<span class="lineNum">     167 </span><span class="lineNoCov">          0 :     if (!is_eltwise) {</span>
<span class="lineNum">     168 </span>            :       Dtype* sum_result = NULL;
<span class="lineNum">     169 </span><span class="lineNoCov">          0 :       if (inner_dim_ == 1) {</span>
<span class="lineNum">     170 </span>            :         sum_result = product;
<span class="lineNum">     171 </span><span class="lineNoCov">          0 :       } else if (sum_result_.count() == 1) {</span>
<span class="lineNum">     172 </span><span class="lineNoCov">          0 :         const Dtype* sum_mult = sum_multiplier_.cpu_data();</span>
<span class="lineNum">     173 </span><span class="lineNoCov">          0 :         Dtype* scale_diff = scale-&gt;mutable_cpu_diff();</span>
<span class="lineNum">     174 </span><span class="lineNoCov">          0 :         if (scale_param) {</span>
<span class="lineNum">     175 </span><span class="lineNoCov">          0 :           Dtype result = caffe_cpu_dot(inner_dim_, product, sum_mult);</span>
<span class="lineNum">     176 </span><span class="lineNoCov">          0 :           *scale_diff += result;</span>
<span class="lineNum">     177 </span>            :         } else {
<span class="lineNum">     178 </span><span class="lineNoCov">          0 :           *scale_diff = caffe_cpu_dot(inner_dim_, product, sum_mult);</span>
<span class="lineNum">     179 </span>            :         }
<span class="lineNum">     180 </span>            :       } else {
<span class="lineNum">     181 </span><span class="lineNoCov">          0 :         const Dtype* sum_mult = sum_multiplier_.cpu_data();</span>
<span class="lineNum">     182 </span><span class="lineNoCov">          0 :         sum_result = (outer_dim_ == 1) ?</span>
<span class="lineNum">     183 </span>            :             scale-&gt;mutable_cpu_diff() : sum_result_.mutable_cpu_data();
<span class="lineNum">     184 </span><span class="lineNoCov">          0 :         caffe_cpu_gemv(CblasNoTrans, sum_result_.count(), inner_dim_,</span>
<span class="lineNum">     185 </span>            :                        Dtype(1), product, sum_mult, Dtype(0), sum_result);
<span class="lineNum">     186 </span>            :       }
<span class="lineNum">     187 </span><span class="lineNoCov">          0 :       if (outer_dim_ != 1) {</span>
<span class="lineNum">     188 </span><span class="lineNoCov">          0 :         const Dtype* sum_mult = sum_multiplier_.cpu_data();</span>
<span class="lineNum">     189 </span><span class="lineNoCov">          0 :         Dtype* scale_diff = scale-&gt;mutable_cpu_diff();</span>
<span class="lineNum">     190 </span><span class="lineNoCov">          0 :         if (scale_dim_ == 1) {</span>
<span class="lineNum">     191 </span><span class="lineNoCov">          0 :           if (scale_param) {</span>
<span class="lineNum">     192 </span><span class="lineNoCov">          0 :             Dtype result = caffe_cpu_dot(outer_dim_, sum_mult, sum_result);</span>
<span class="lineNum">     193 </span><span class="lineNoCov">          0 :             *scale_diff += result;</span>
<span class="lineNum">     194 </span>            :           } else {
<span class="lineNum">     195 </span><span class="lineNoCov">          0 :             *scale_diff = caffe_cpu_dot(outer_dim_, sum_mult, sum_result);</span>
<span class="lineNum">     196 </span>            :           }
<span class="lineNum">     197 </span>            :         } else {
<span class="lineNum">     198 </span><span class="lineNoCov">          0 :           caffe_cpu_gemv(CblasTrans, outer_dim_, scale_dim_,</span>
<span class="lineNum">     199 </span>            :                          Dtype(1), sum_result, sum_mult, Dtype(scale_param),
<span class="lineNum">     200 </span>            :                          scale_diff);
<span class="lineNum">     201 </span>            :         }
<span class="lineNum">     202 </span>            :       }
<span class="lineNum">     203 </span>            :     }
<span class="lineNum">     204 </span>            :   }
<span class="lineNum">     205 </span><span class="lineNoCov">          0 :   if (propagate_down[0]) {</span>
<span class="lineNum">     206 </span><span class="lineNoCov">          0 :     const Dtype* top_diff = top[0]-&gt;cpu_diff();</span>
<span class="lineNum">     207 </span><span class="lineNoCov">          0 :     const Dtype* scale_data = scale-&gt;cpu_data();</span>
<span class="lineNum">     208 </span><span class="lineNoCov">          0 :     Dtype* bottom_diff = bottom[0]-&gt;mutable_cpu_diff();</span>
<span class="lineNum">     209 </span><span class="lineNoCov">          0 :     for (int n = 0; n &lt; outer_dim_; ++n) {</span>
<span class="lineNum">     210 </span><span class="lineNoCov">          0 :       for (int d = 0; d &lt; scale_dim_; ++d) {</span>
<span class="lineNum">     211 </span><span class="lineNoCov">          0 :         const Dtype factor = scale_data[d];</span>
<span class="lineNum">     212 </span><span class="lineNoCov">          0 :         caffe_cpu_scale(inner_dim_, factor, top_diff, bottom_diff);</span>
<span class="lineNum">     213 </span><span class="lineNoCov">          0 :         bottom_diff += inner_dim_;</span>
<span class="lineNum">     214 </span><span class="lineNoCov">          0 :         top_diff += inner_dim_;</span>
<span class="lineNum">     215 </span>            :       }
<span class="lineNum">     216 </span>            :     }
<span class="lineNum">     217 </span>            :   }
<span class="lineNum">     218 </span><span class="lineNoCov">          0 : }</span>
<a name="219"><span class="lineNum">     219 </span>            : </a>
<span class="lineNum">     220 </span>            : #ifdef CPU_ONLY
<span class="lineNum">     221 </span><span class="lineNoCov">          0 : STUB_GPU(ScaleLayer);</span>
<span class="lineNum">     222 </span>            : #endif
<a name="223"><span class="lineNum">     223 </span>            : </a>
<span class="lineNum">     224 </span>            : INSTANTIATE_CLASS(ScaleLayer);
<a name="225"><span class="lineNum">     225 </span><span class="lineCov">          3 : REGISTER_LAYER_CLASS(Scale);</span></a>
<span class="lineNum">     226 </span>            : 
<span class="lineNum">     227 </span><span class="lineCov">          3 : }  // namespace caffe</span>
</pre>
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